The stochastic policy of discrete control not only gives a possible optimal solution but also enables a soft continuous learning process. Traditional policy gradient for discrete control utilizes likelihood ratio methods which incorporate probability distribution of action, e.g. REINFORCE [1]}. The idea is to weight the probability of action by the reward based on this action. The state-of-the-art actor-critic algorithm A3C [2]} is more along this line.
